Abstract
and choose from. There is no guarantee that the chosen descriptors are relevant for a given collection. In addition,this method does not scale to longer samples that require higher-dimensional descriptions, which biases systems towards the use of short samples. In this paper we propose novel framework for automatic creation of interactive sound spaces from sound collections using feature learning and dimensionality reduction. The framework is implemented as a
software library using the SuperCollider language. We compare several algorithms and describe some example interfaces for interacting with the resulting spaces. Our experiments signal the potential of unsupervised algorithms for creating data-driven musical interfaces.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Conference on New Interfaces for Musical Expression |
| Editors | Marcelo Queiroz, Anna Xambó Sedó |
| Place of Publication | Porto Alegre |
| Pages | 313-318 |
| Number of pages | 6 |
| Publication status | Published - Jun 2019 |
| Event | The International Conference on New Interfaces for Musical Expression - Porto Alegre, Brazil Duration: 3 Jun 2019 → 6 Jun 2019 https://www.nime.org/ |
Publication series
| Name | Proceedings of the conference on New Interface for Musical Expression (NIME) |
|---|---|
| ISSN (Print) | 2220-4806 |
Conference
| Conference | The International Conference on New Interfaces for Musical Expression |
|---|---|
| Abbreviated title | NIME |
| Country | Brazil |
| City | Porto Alegre |
| Period | 3/06/19 → 6/06/19 |
| Internet address |
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Adaptive Mapping of Sound Collections for Data-driven Musical Interfaces. / Roma, Gerard; Green, Owen; Tremblay, Pierre Alexandre.
Proceedings of the International Conference on New Interfaces for Musical Expression. ed. / Marcelo Queiroz; Anna Xambó Sedó. Porto Alegre, 2019. p. 313-318 (Proceedings of the conference on New Interface for Musical Expression (NIME)).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Adaptive Mapping of Sound Collections for Data-driven Musical Interfaces
AU - Roma, Gerard
AU - Green, Owen
AU - Tremblay, Pierre Alexandre
PY - 2019/6
Y1 - 2019/6
N2 - Descriptor spaces have become an ubiquitous interaction paradigm for music based on collections of audio samples. However, most systems rely on a small predefined set of descriptors, which the user is often required to understandand choose from. There is no guarantee that the chosen descriptors are relevant for a given collection. In addition,this method does not scale to longer samples that require higher-dimensional descriptions, which biases systems towards the use of short samples. In this paper we propose novel framework for automatic creation of interactive sound spaces from sound collections using feature learning and dimensionality reduction. The framework is implemented as asoftware library using the SuperCollider language. We compare several algorithms and describe some example interfaces for interacting with the resulting spaces. Our experiments signal the potential of unsupervised algorithms for creating data-driven musical interfaces.
AB - Descriptor spaces have become an ubiquitous interaction paradigm for music based on collections of audio samples. However, most systems rely on a small predefined set of descriptors, which the user is often required to understandand choose from. There is no guarantee that the chosen descriptors are relevant for a given collection. In addition,this method does not scale to longer samples that require higher-dimensional descriptions, which biases systems towards the use of short samples. In this paper we propose novel framework for automatic creation of interactive sound spaces from sound collections using feature learning and dimensionality reduction. The framework is implemented as asoftware library using the SuperCollider language. We compare several algorithms and describe some example interfaces for interacting with the resulting spaces. Our experiments signal the potential of unsupervised algorithms for creating data-driven musical interfaces.
KW - Dimensionality reduction
KW - feature learning
KW - information visualisation
UR - https://www.nime.org/
M3 - Conference contribution
T3 - Proceedings of the conference on New Interface for Musical Expression (NIME)
SP - 313
EP - 318
BT - Proceedings of the International Conference on New Interfaces for Musical Expression
A2 - Queiroz, Marcelo
A2 - Sedó, Anna Xambó
CY - Porto Alegre
ER -